Classic psychological theories have demonstrated the power and limitations of spatial representations, providing geometric tools for reasoning about the similarity of objects and showing that human intuitions sometimes violate the constraints of geometric spaces. Recent machine learning methods for deriving vector-space embeddings of words have begun to garner attention for their surprising capacity to capture simple analogies consistently across large corpora, giving new life to a classic model of analogies as parallelograms that was first proposed and briefly explored by psychologists. We evaluate the parallelogram model of analogy as applied to modern data-driven word embeddings, providing a detailed analysis of the extent to which this approach captures human behavior in the domain of word pairs. Using a large novel benchmark dataset of human analogy completions, we show that word similarity alone surprisingly captures some aspects of human responses better than the parallelogram model. To gain a fine-grained picture of how well these models predict relational similarity, we also collect a large dataset of human relational similarity judgments and find that the parallelogram model captures some semantic relationships better than others. Finally, we provide evidence for deeper limitations of the parallelogram model of analogy based on the intrinsic geometric constraints of vector spaces, paralleling classic results for item similarity. Taken together, these results show that while modern word embeddings do an impressive job of capturing semantic similarity at scale, the parallelogram model alone is insufficient to account for how people form even the simplest analogies.

经典的心理学理论已经证明了空间表征的力量和局限性，为推理物体的相似性提供了几何工具，并表明人类的直觉有时违反了几何空间的约束。近年来，用于导出单词向量空间嵌入的机器学习方法因其惊人的能力来捕获大型语料库中的简单类比而获得了广泛的关注，这为心理学家首次提出并简要探讨的平行四边形的经典类比模型赋予了新的生命。 。我们评估了应用于现代数据驱动词嵌入的类比的平行四边形模型，提供了对该方法在词对域中捕获人类行为的程度的详细分析。使用大型新颖的人类类比补全基准数据集，我们显示出单词相似性比平行四边形模型更令人惊讶地捕获了人类反应的某些方面。为了获得这些模型如何很好地预测关系相似性的细粒度图片，我们还收集了人类关系相似性判断的大型数据集，并发现平行四边形模型比其他模型更好地捕获了某些语义关系。最后，我们基于向量空间的内在几何约束为平行四边形模型的类比提供了更深层次的限制的证据，并与经典结果相似。综上所述，这些结果表明，尽管现代单词嵌入在大规模捕获语义相似性方面做得非常出色，